Monitoring milling quality of rice by image analysis

School of Environment, Resources and Development (SERD), Asian Institute of Technology, Krung Thep, Bangkok, Thailand
Computers and Electronics in Agriculture (Impact Factor: 1.76). 12/2001; 33(1):19-33. DOI: 10.1016/S0168-1699(01)00169-7


Rough rice is milled to produce polished edible grain by first subjecting to dehusking or removal of hulls and then to the removal of brownish outer bran layer known as whitening. The control of whiteness (degree of milling) and percentage of broken kernels in milled rice is required to minimize the economic loss to the millers. Digital image analysis was used to determine the head rice yield (HRY), representing the proportion by weight of milled kernels with three quarters or more of their original length, and the whiteness of milled rice. Ten varieties of Thai rice were subjected to varying degrees of milling by adjusting the test duration from 0.5 to 2.5 min. Three-dimensional features (namely, length, perimeter and projected area) were extracted from the images of individual kernels in a milled sample and used to compute a characteristic dimension ratio (CDR) defined as the ratio of the sum of a particular dimensional feature of all head rice kernels to that of all kernels comprising head and broken rice in the sample. HRY and CDR were found to be related by power functions based on the above-mentioned dimensional features, with R2 more than 0.99 in all cases. The CDR based on the projected area of kernels in their natural rest position provided the best estimate of the HRY with the lowest root mean square error of 1.1% among all dimensional features studied. In case of the whiteness of milled samples, the values provided by a commercial whiteness meter and the mean of gray level distribution determined by image analysis correlated with an R2 value of 0.99. The results of this study showed that two-dimensional imaging of milled rice kernels could be used for making quantitative assessment of HRY and degree of milling for on-line monitoring and better control of the rice milling operation.

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    • "Student's t test indicated that the mean reflectance values of the bran pixels significantly differed from those of the endosperm pixels in the selected spectral range (P < 0.05). This result verified the concept of measuring rice DOM based on surface light reflectance (Yadav and Jindal 2001) and justified the use of HSI for assessing residual bran distribution on rice surface. The mean reflectance values of the bran and endosperm pixels throughout the selected spectral range (485–930 nm) were 0.50 and 0.32, respectively. "
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    ABSTRACT: Residual bran on milled rice is directly related to its quality. This study proposes a method to measure the residual bran patterns on a single rice grain by using hyperspectral imaging (HSI). HSI is a sensing technique that combines both spatial and spectral information and may be used for chemical compound identification and quantification. In this study, HSI was applied to assess rice bran residue nondestructively. In the experiment, rice samples were milled and scanned with an HSI system. Afterward, the rice samples were dyed to enable the residual bran to be identified with optical microscopy and image processing algorithms. Classifiers were then developed to predict the rice bran residue by using the HSI measurements as inputs. The predicted images were compared with the micrograph images for classifier performance evaluation. The proposed approach can estimate the residual bran distribution on milled rice surface with an accuracy of 93.5%.
    Full-text · Article · Nov 2014 · Cereal Chemistry
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    • "Several researchers (Abud-Archila et al., 2000; Bello et al., 2006; Chen et al., 1997; Clement and Seguy, 1994; Reid et al., 1998; Sarker and Farouk, 1989; Yadav and Jindal, 2001; Yang et al.,) determined some milling quality of rice grains . "
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    ABSTRACT: Milling, an important processing step of rough rice, is usually done to produce white, polished grains. In this paper the quality of 22 milled rice varieties, common in Mazandaran, Iran, are investigated. These rice varieties included local varieties and breeding lines. Parameters assessed were head rice yield, degree of milling, husk removed percent, and total milling recovery. Results obtained revealed that the Tarom Mahali and Champa varieties have the highest head rice yield as 60.58 and 66.39 % and total milling recovery as 69.96 and 71.38 %, respectively. The greatest degree of milling value was found for the Haraz variety with a mean of 16.06 %. Also, it was found that the husk removed percent values were not statistically different among the varieties studied. Finally, considering all results obtained, the varieties of Tarom Mahali, Champa, and Neda showed to be more economical in the milling process.
    Full-text · Article · Jan 2010 · International Journal of Food Engineering
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    • "Recent research has shown that machine vision has the potential to become a viable tool for rice quality inspection, most of these studies have utilized well-defined images of rice kernels acquired under controlled conditions. Under controlled situations, rice kernels are usually placed apart from each other manually or by other means during image acquisition (Yadav and Jindal, 2001; Igathinathane et al., 2008). However, it is quite time consuming and/or not always practical to separate the rice prior to imaging. "
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    ABSTRACT: A novel algorithm based on watershed and concavities is proposed to segment the clustered slender-particles, such as the clustered rice kernels. First, the distance and watershed transform is used to the binary image of clustered slender-particles. Secondly, the watershed post-processing of over-segmentation is dealt with by utilizing concavity features of related shapes. Thirdly, the candidate splitting lines of touching clusters is found by matching the concavities to the un-segmentations left. Finally, the supplementary criterions are applied, such as the shortest distance, the opposite orientation, the splitting path orientation, etc., to determine whether a candidate splitting line can be accepted or not. Experimental results show that the algorithm can segment the large-scale clustered slender-particles efficiently, where such a quantitative analysis was previously infeasible.
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